7 citations · 17 across the 7 of their papers we have counts for
7 papers
Efficient Document Retrieval by End-to-End Refining and Quantizing BERT Embedding with Contrastive Product Quantization
Zexuan Qiu, Qinliang Su, Jianxing Yu +1
Efficient document retrieval heavily relies on the technique of semantic hashing, which learns a binary code for every document and employs Hamming distance to evaluate document di…
Refining BERT Embeddings for Document Hashing via Mutual Information Maximization
Zijing Ou, Qinliang Su, Jianxing Yu +3
Existing unsupervised document hashing methods are mostly established on generative models. Due to the difficulties of capturing long dependency structures, these methods rarely mo…
Retrieve & Memorize: Dialog Policy Learning with Multi-Action Memory
Yunhao Li, Yunyi Yang, Xiaojun Quan +1
Dialogue policy learning, a subtask that determines the content of system response generation and then the degree of task completion, is essential for task-oriented dialogue system…
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval
Zijing Ou, Qinliang Su, Jianxing Yu +5
With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale information retrieval. To generate high-quality ha…
Unsupervised Hashing with Contrastive Information Bottleneck
Zexuan Qiu, Qinliang Su, Zijing Ou +2
Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much informatio…
Embedding Dynamic Attributed Networks by Modeling the Evolution Processes
Zenan Xu, Zijing Ou, Qinliang Su +3
Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the…